Nearest feature line classifier based on collaborative representation with nearest neighbour search algorithm

Abstract Nearest feature line is an effective classification algorithm. However, if a test sample cannot be linear represented by the training samples, the algorithm may not work very well. Moreover, another problem is that it will have a large computation complexity. Therefore, the authors propose...

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Autores principales:	Zhongjie Zhuang, Jeng‐Shyang Pan, Shu‐Chuan Chu, Hao Luo
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/5ac79b9207124319a05bb0c70d971274
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id	oai:doaj.org-article:5ac79b9207124319a05bb0c70d971274
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spelling	oai:doaj.org-article:5ac79b9207124319a05bb0c70d9712742021-11-16T10:18:22ZNearest feature line classifier based on collaborative representation with nearest neighbour search algorithm1350-911X0013-519410.1049/ell2.12030https://doaj.org/article/5ac79b9207124319a05bb0c70d9712742021-01-01T00:00:00Zhttps://doi.org/10.1049/ell2.12030https://doaj.org/toc/0013-5194https://doaj.org/toc/1350-911XAbstract Nearest feature line is an effective classification algorithm. However, if a test sample cannot be linear represented by the training samples, the algorithm may not work very well. Moreover, another problem is that it will have a large computation complexity. Therefore, the authors propose a novel algorithm. To begin with, the test sample is linear represented by all the training samples, and the errors between the test sample and every training sample are calculated. The authors only keep the training samples with small errors. In this way, on the one hand, training samples are not suitable for the test sample will be ignored, on the other hand, running time can be reduced. To further reduce the computing time of the algorithm, nearest neighbour search technique is applied to the algorithm. Experiments on numerical and image database show the algorithm cannot only improve the classification accuracy, but also reduce runtime.Zhongjie ZhuangJeng‐Shyang PanShu‐Chuan ChuHao LuoWileyarticleElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENElectronics Letters, Vol 57, Iss 1, Pp 20-22 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Electrical engineering. Electronics. Nuclear engineering TK1-9971 Zhongjie Zhuang Jeng‐Shyang Pan Shu‐Chuan Chu Hao Luo Nearest feature line classifier based on collaborative representation with nearest neighbour search algorithm
description	Abstract Nearest feature line is an effective classification algorithm. However, if a test sample cannot be linear represented by the training samples, the algorithm may not work very well. Moreover, another problem is that it will have a large computation complexity. Therefore, the authors propose a novel algorithm. To begin with, the test sample is linear represented by all the training samples, and the errors between the test sample and every training sample are calculated. The authors only keep the training samples with small errors. In this way, on the one hand, training samples are not suitable for the test sample will be ignored, on the other hand, running time can be reduced. To further reduce the computing time of the algorithm, nearest neighbour search technique is applied to the algorithm. Experiments on numerical and image database show the algorithm cannot only improve the classification accuracy, but also reduce runtime.
format	article
author	Zhongjie Zhuang Jeng‐Shyang Pan Shu‐Chuan Chu Hao Luo
author_facet	Zhongjie Zhuang Jeng‐Shyang Pan Shu‐Chuan Chu Hao Luo
author_sort	Zhongjie Zhuang
title	Nearest feature line classifier based on collaborative representation with nearest neighbour search algorithm
title_short	Nearest feature line classifier based on collaborative representation with nearest neighbour search algorithm
title_full	Nearest feature line classifier based on collaborative representation with nearest neighbour search algorithm
title_fullStr	Nearest feature line classifier based on collaborative representation with nearest neighbour search algorithm
title_full_unstemmed	Nearest feature line classifier based on collaborative representation with nearest neighbour search algorithm
title_sort	nearest feature line classifier based on collaborative representation with nearest neighbour search algorithm
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/5ac79b9207124319a05bb0c70d971274
work_keys_str_mv	AT zhongjiezhuang nearestfeaturelineclassifierbasedoncollaborativerepresentationwithnearestneighboursearchalgorithm AT jengshyangpan nearestfeaturelineclassifierbasedoncollaborativerepresentationwithnearestneighboursearchalgorithm AT shuchuanchu nearestfeaturelineclassifierbasedoncollaborativerepresentationwithnearestneighboursearchalgorithm AT haoluo nearestfeaturelineclassifierbasedoncollaborativerepresentationwithnearestneighboursearchalgorithm
_version_	1718426547961987072

Nearest feature line classifier based on collaborative representation with nearest neighbour search algorithm

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